Contextual guidance of eye movements and attention in real-world scenes: The role of global features in object search.

TLDR

Human visual search in natural scenes relies heavily on contextual information, yet formal modeling of these influences remains unresolved. The study proposes a Bayesian model for attentional guidance that leverages global scene context. The model employs two parallel pathways—one computing local saliency and the other extracting global scene features—and integrates bottom‑up, contextual, and top‑down signals to predict likely fixation regions. The integrated model accurately predicts fixation locations in natural search tasks, showing that combining saliency, scene context, and top‑down cues improves attentional guidance.

Abstract

Many experiments have shown that the human visual system makes extensive use of contextual information for facilitating object search in natural scenes. However, the question of how to formally model contextual influences is still open. On the basis of a Bayesian framework, the authors present an original approach of attentional guidance by global scene context. The model comprises 2 parallel pathways; one pathway computes local features (saliency) and the other computes global (scene-centered) features. The contextual guidance model of attention combines bottom-up saliency, scene context, and top-down mechanisms at an early stage of visual processing and predicts the image regions likely to be fixated by human observers performing natural search tasks in real-world scenes.